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AM3F-FlowNet:基于注意力的多规模多分支流量网络
Chenghao Fu1, Wenzhong Yang1,2, Danny Chen1
1School of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了基于注意力的网络来分析微表达式,通过整合多模光流信息和解决类不平衡,从有限的数据中有效地学习运动特征.
科学领域:
- 计算机视觉 计算机视觉
- 情感计算是一种情感计算.
- 机器学习 机器学习
背景情况:
- 微表情是短暂的面部变化,表明情绪,但它们的注释复杂性导致数据稀缺.
- 从有限的微表达式数据集中提取有意义的特征是具有挑战性的.
研究的目的:
- 从有限的数据中开发一种有效的方法来学习微表达特征.
- 通过利用多模式光流和注意力机制来提高微表情识别的准确性.
主要方法:
- 提出了一种基于注意力的多规模,多模式,多分支的流量网络.
- 提取的水平光流,垂直光流和光应变.
- 利用空间和道注意力来进行特征融合和重量调整.
- 引入了对数调整的先验知识权重损失,以处理阶级不平衡.
主要成果:
- 拟议的网络有效地从微表达式视频中学习运动信息.
- 多尺度融合和特征重权化模块增强了特征表示.
- 新的损失函数减轻了不平衡的微表达样本的影响.
- 对基准数据集的实验结果表明,性能与最先进的方法相美.
结论:
- 开发的基于注意力的网络为有限数据的微表达式分析提供了强大的解决方案.
- 多模态光流和高级注意力机制的整合显著改善了特征学习.
- 拟议的损失函数有效地解决了微表达式识别中类失衡的挑战.
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